andygrove opened a new issue, #5231: URL: https://github.com/apache/datafusion-comet/issues/5231
Triage pass over the open `requires-triage` queue, per the project [Bug Triage Guide](https://github.com/apache/datafusion-comet/blob/main/docs/source/contributor-guide/bug_triage.md). - Date: 2026-08-03 - Total issues processed: 67 (62 triaged, 5 skipped, 0 failed) - Type counts: 23 bugs, 39 enhancements - Priority counts applied: `priority:critical` 2, `priority:high` 6, `priority:medium` 12, `priority:low` 3 - Guide: [docs/source/contributor-guide/bug_triage.md](https://github.com/apache/datafusion-comet/blob/main/docs/source/contributor-guide/bug_triage.md) Labels have already been applied. A reviewer should spot-check the calls below and close this issue when satisfied; corrections should be made directly on the affected issue. Two notes on label availability: the guide lists `spark 4` as a pre-existing area indicator, but the repository only has `spark 4.0` / `spark 4.1` / `spark 4.2`, so no `spark 4` label was applied anywhere in this pass. Where an issue already carried non-guide labels (`correctness`, `performance`, `EPIC`, `area:udf`, `area:Iceberg`, `temporal expressions`, `test`, `user experience`, `good first issue`), those were left untouched and are included in the area lists below for context. ## Bugs ### priority:critical - Codegen dispatcher: whole-tree NullIntolerant short-circuit suppresses ANSI errors, plus TIME type gaps between canHandle and the runtime dispatcher ([#5218](https://github.com/apache/datafusion-comet/issues/5218)) - Area labels: `area:expressions`, `area:udf`, `correctness` - Rationale: Finding 1 makes Comet return NULL on a row where Spark raises an ANSI error, with no error or warning to the user, which the guide's decision tree places at `priority:critical`; this replaces the reporter's initial `priority:high`, and the blast radius is wide because ~70 built-in expressions route through `CometCodegenDispatch`. - [EPIC] cast from string: trim semantics diverge from Spark across all numeric, datetime and boolean targets ([#5149](https://github.com/apache/datafusion-comet/issues/5149)) - Area labels: `area:expressions`, `correctness`, `EPIC` - Rationale: 7 of the 8 supported string-cast targets diverge in both directions, including Comet returning a non-NULL value where Spark returns NULL or throws — silent wrong results across a core expression family. ### priority:high - ListPositionsExpr panics on sliced list input, breaking native posexplode over LIMIT with OFFSET ([#5224](https://github.com/apache/datafusion-comet/issues/5224)) - Area labels: `area:expressions` - Rationale: Reproducible native panic (`Result::unwrap()` on `Err`) on a supported path — native `posexplode` fed by a limit with a non-zero offset — matching the guide's crash rule; the failure is visible, so it stays off critical. - [EPIC] Memory pool and accounting audit sweep ([#5212](https://github.com/apache/datafusion-comet/issues/5212)) - Area labels: `area:shuffle`, `area:ffi`, `correctness`, `performance`, `EPIC` - Rationale: The two headline findings are defects in the **default** off-heap pool — a task can never exceed `pool_size / num_consumers`, and per-task pool entries leak for the executor's lifetime — which is major functional breakage of production memory provisioning rather than a wrong-results bug. - collect_list/collect_set can fail with "column types must match schema types" on nested-field nullability drift ([#5158](https://github.com/apache/datafusion-comet/issues/5158)) - Area labels: `area:aggregation`, `area:expressions` - Rationale: The aggregate hard-fails validating its own output on a supported path (reachable via `CreateNamedStruct`, whose `evaluate()` and `data_type()` types can diverge), so the query dies rather than falling back; visible failure keeps it below critical. - ShuffleScanExec and ExpandExec assert on nested field nullability instead of normalizing it ([#5137](https://github.com/apache/datafusion-comet/issues/5137)) - Area labels: `area:shuffle`, `area:ffi` - Rationale: A single mismatched nested `nullable` flag hard-fails the operator even though the data itself is fine — major functional breakage at the FFI/shuffle boundary, per the guide's `priority:high` definition. - Hashing a CalendarInterval value fails with "Unsupported data type in hasher: Interval(MonthDayNano)" ([#5059](https://github.com/apache/datafusion-comet/issues/5059)) - Area labels: `area:expressions` - Rationale: The plan-time gate now advertises `CalendarIntervalType` as supported, so the hash is serialized to native and the query dies with a `CometNativeException` instead of falling back — a regression from #4898 that turns a clean fallback into a hard failure. - Null CalendarInterval literal throws native exception instead of evaluating to null ([#5058](https://github.com/apache/datafusion-comet/issues/5058)) - Area labels: `area:expressions` - Rationale: Same shape as #5059 — the type passes `isSupportedDataType` but `create_null_literal` has no `Interval` arm, so a null literal fails the query at execution time instead of falling back. ### priority:medium - Native make_date rejects Spark-valid years outside chrono range ([#5208](https://github.com/apache/datafusion-comet/issues/5208)) - Area labels: `area:expressions` - Rationale: Comet returns NULL (non-ANSI) or throws where Spark returns a valid date, but only for years beyond chrono's ±262143 range, so the practical surface is negligible; kept at `priority:medium` with an escalation note below rather than critical. - spark.comet.exceptionOnDatetimeRebase is dead code: wire it up or remove it ([#5195](https://github.com/apache/datafusion-comet/issues/5195)) - Area labels: `area:scan` - Rationale: A user-facing, documented config that promises an exception on legacy-calendar data is never read, so users get silence and wrong results instead; a broken feature with a workaround (`spark.comet.scan.enabled=false`) → `priority:medium`. - Native Parquet schema-on-read diverges from Spark for ANSI interval targets ([#5188](https://github.com/apache/datafusion-comet/issues/5188)) - Area labels: `area:scan` - Rationale: Comet reinterprets an INT column as microseconds for a read Spark rejects outright with `PARQUET_COLUMN_DATA_TYPE_MISMATCH`; no query Spark itself accepts returns wrong data, so it is a broken-compatibility case rather than critical — see Escalations. - fix: string-to-timestamp does not trim ISO control characters, and leading '+' returns null under ANSI ([#5165](https://github.com/apache/datafusion-comet/issues/5165)) - Area labels: `area:expressions` - Rationale: Comet returns NULL where Spark parses successfully (C0 control characters) and misses an ANSI `CAST_INVALID_INPUT`; the surface is limited to control-character-padded input and one ANSI rejection path, so `priority:medium` with an escalation note. - [Bug] Native make_interval overflows or loses precision for valid Spark seconds ([#5131](https://github.com/apache/datafusion-comet/issues/5131)) - Area labels: `area:expressions` - Rationale: Genuine precision loss and overflow versus Spark, but `CometMakeInterval` is marked Incompatible by default so the native path requires explicit opt-in — no exposure in the default configuration. - Native columnar-to-row conversion is much slower than the JVM implementation for small batches ([#5112](https://github.com/apache/datafusion-comet/issues/5112)) - Area labels: `area:ffi`, `performance` - Rationale: Reclassified from `enhancement` to `bug`: the native converter is enabled by default and measures 3.7x–15.7x slower per row than the JVM path it replaced, which the guide lists as a performance regression at `priority:medium` (workaround: disable the native converter). - negative: ANSI overflow check reads null slots and can raise spurious overflow errors ([#5093](https://github.com/apache/datafusion-comet/issues/5093)) - Area labels: `area:expressions` - Rationale: A `MIN` value left in a null slot raises a spurious ARITHMETIC_OVERFLOW where Spark returns null — a visible functional bug that requires the null slot's residual bytes to be exactly `MIN`, so `priority:medium` rather than high. - [EPIC] ANSI mode audit follow-ups (vs Spark 4.1.1) ([#5078](https://github.com/apache/datafusion-comet/issues/5078)) - Area labels: `area:expressions`, `EPIC` - Rationale: Tracker for a filed set of children; all of its silent-wrong-result children (#5065, #5066, #5067, #5070, #5075) are already closed, so what remains open is error-fidelity work (#5071–#5073, medium tier) plus hardening and test coverage — the EPIC is set to the highest remaining child tier. - next_day and make_date ANSI errors surface as CometNativeException instead of Spark exception classes ([#5073](https://github.com/apache/datafusion-comet/issues/5073)) - Area labels: `area:expressions`, `correctness`, `temporal expressions` - Rationale: The throw/NULL decision is already correct; only the exception type, error class, and SQLSTATE differ, so nothing is silent — a functional bug affecting users who match on Spark error conditions. - Native ANSI errors raised as Arrow errors bypass SparkError conversion (wide decimal, decimal divide, decimal-to-decimal cast) ([#5072](https://github.com/apache/datafusion-comet/issues/5072)) - Area labels: `area:expressions`, `correctness` - Rationale: Same class as #5073 — the error is raised, but as a generic `CometNativeException` without the Spark error class, SQLSTATE, or query context; visible failure, so `priority:medium`. - ANSI arithmetic overflow errors: wrong error class for Byte/Short, wrong type names, missing try_ suggestions ([#5071](https://github.com/apache/datafusion-comet/issues/5071)) - Area labels: `area:expressions`, `correctness` - Rationale: Error-fidelity only: Comet raises `ARITHMETIC_OVERFLOW` where Spark raises `BINARY_ARITHMETIC_OVERFLOW`, names Long overflow "integer", and omits the `try_` suggestion. The overflow itself is detected correctly. - Cast from boolean to decimal ignores eval mode: throws where Spark returns NULL in legacy/try mode ([#5068](https://github.com/apache/datafusion-comet/issues/5068)) - Area labels: `area:expressions`, `correctness` - Rationale: The pair is marked `Compatible()` in all eval modes but the native kernel takes no `eval_mode`, so Comet throws where Spark returns NULL on Spark 3.x, with ANSI off, and for `try_cast`; the divergence is a visible error rather than a silent wrong value. ### priority:low - Extended explain operator stats miscount reuse wrappers and CometSubqueryBroadcast ([#5203](https://github.com/apache/datafusion-comet/issues/5203)) - Area labels: none - Rationale: Affects only the `Comet accelerated N out of M eligible operators` line in extended explain output; query results and execution are unaffected — cosmetic/reporting per the guide. - Expression explain tags leak between unrelated plans via the shared Literal.TrueLiteral singleton ([#5229](https://github.com/apache/datafusion-comet/issues/5229)) - Area labels: none - Rationale: A `[COMET-INFO: ...]` message is attached to an unrelated operator in an unrelated query; misleading diagnostics only, with no effect on planning decisions or results. - cast_map_to_map drops entries null buffer and ignores target sorted flag ([#5097](https://github.com/apache/datafusion-comet/issues/5097)) - Area labels: `area:expressions` - Rationale: The reporter states both defects are latent today — Spark-produced maps carry no null map entries, and the `sorted` flag is metadata-only — so nothing is observable through normal plans; a minor issue per the guide. ## Enhancements - Deduplicate Throwable cause-chain traversal in Comet tests ([#5223](https://github.com/apache/datafusion-comet/issues/5223)) - Area labels: none - Rationale: Refactor that lifts a duplicated `causeChain` helper into `CometTestBase`; test-code cleanup with no behavior change. - Enable SPARK-57298 collect_set tests when Spark 4.2 SQL diff lands ([#5209](https://github.com/apache/datafusion-comet/issues/5209)) - Area labels: `area:aggregation`, `spark sql tests` - Rationale: Test-enablement task blocked on a Spark 4.2 diff and CI job that do not exist yet; nothing is broken today. - Do not accelerate a query stage that contains a columnar-to-row fallback (enable whole-stage revert by default) ([#5207](https://github.com/apache/datafusion-comet/issues/5207)) - Area labels: `performance`, `user experience` - Rationale: Proposes flipping `RevertNativeForTransitionHeavyStages` on by default and making its strictest setting usable — a default/config change to improve out-of-the-box behavior. - [EPIC] Planner, serde, and optimizer rule performance audit ([#5199](https://github.com/apache/datafusion-comet/issues/5199)) - Area labels: `performance`, `EPIC` - Rationale: Collects driver-side planner and serde optimization opportunities from a profiling pass; performance work, which the guide classifies as enhancement. - Shuffle performance audit: candidate optimizations needing investigation ([#5198](https://github.com/apache/datafusion-comet/issues/5198)) - Area labels: `area:shuffle`, `performance` - Rationale: An explicitly unbenchmarked work list of shuffle optimization candidates — investigation and optimization, not a defect. - Avoid duplicate CheckOverflow evaluation for decimal division ([#5190](https://github.com/apache/datafusion-comet/issues/5190)) - Area labels: `area:expressions` - Rationale: Two nested equivalent `CheckOverflow` wrappers both scan the batch; removing the redundant pass is a performance optimization with identical results. - Fall back instead of asserting when native scan partition values cannot be serialized ([#5189](https://github.com/apache/datafusion-comet/issues/5189)) - Area labels: `area:scan` - Rationale: Hardening of an invariant — the reporter states there is no currently known unsupported partition literal that passes the gate, so this prevents a future failure rather than fixing a live one. - Revisit CometCastSuite assumptions now that Cast has a codegen dispatch fallback ([#5186](https://github.com/apache/datafusion-comet/issues/5186)) - Area labels: `area:expressions` - Rationale: Test-suite modernization: assumptions written before `CodegenDispatchFallback` now force working coverage to stay ignored. - [DISCUSS] Is Comet ready to move to top-level Apache Comet project? ([#5184](https://github.com/apache/datafusion-comet/issues/5184)) - Area labels: none - Rationale: Project-governance discussion thread, not a defect; classified as enhancement because the guide's type labels are exhaustive and nothing is broken. - Make Comet even friendlier to agentic development ([#5178](https://github.com/apache/datafusion-comet/issues/5178)) - Area labels: none - Rationale: Planned contributor-guide and skill improvements — documentation and tooling additions. - [EPIC] Optimize native cast expression kernels ([#5128](https://github.com/apache/datafusion-comet/issues/5128)) - Area labels: `area:expressions`, `performance`, `EPIC` - Rationale: Tracks per-row cost removal in the native cast kernels with a bit-identical-output requirement; pure optimization work. - Support inline and stack generators in GenerateExec ([#5125](https://github.com/apache/datafusion-comet/issues/5125)) - Area labels: `area:expressions` - Rationale: New generator support (`inline`, `stack`) that currently falls back to Spark as designed — added functionality. - Add native support for CollectMetricsExec (df.observe) to preserve stage fusion ([#5124](https://github.com/apache/datafusion-comet/issues/5124)) - Area labels: none - Rationale: New native operator for an operator that is not yet expected to run natively. - Extend native Arrow UDF path to grouped-aggregate, window, and applyInArrow Python operators ([#5123](https://github.com/apache/datafusion-comet/issues/5123)) - Area labels: `area:udf` - Rationale: Extends an existing opt-in native path to additional Python operators that currently fall back by design. - Accelerate row-level MERGE / UPDATE / DELETE plans (MergeRowsExec, ReplaceDataExec, WriteDeltaExec) ([#5122](https://github.com/apache/datafusion-comet/issues/5122)) - Area labels: `area:writer`, `area:Iceberg` - Rationale: New operator support for row-level plans that fall back entirely today. - Accelerate DataSource V2 writes (AppendDataExec, OverwriteByExpressionExec, OverwritePartitionsDynamicExec) ([#5121](https://github.com/apache/datafusion-comet/issues/5121)) - Area labels: `area:writer`, `area:Iceberg` - Rationale: New write-path support; DSv2 writes are not yet expected to be accelerated. - Optimize JVM columnar-to-row conversion ([#5119](https://github.com/apache/datafusion-comet/issues/5119)) - Area labels: `performance`, `EPIC` - Rationale: Removes measured allocation and virtual-dispatch costs from the interpreted JVM C2R path; output is unchanged. - Use Spark ResourceProfiles to give native and JVM stages independent memory configs ([#5116](https://github.com/apache/datafusion-comet/issues/5116)) - Area labels: none - Rationale: New capability enabled by stage-based fallback (#4519) — per-stage memory provisioning that does not exist today. - Add support for SampleExec (DataFrame.sample, TABLESAMPLE, randomSplit) ([#5109](https://github.com/apache/datafusion-comet/issues/5109)) - Area labels: none - Rationale: New operator support; `SampleExec` currently falls back as designed. - [EPIC] Replace hand-rolled native code with existing arrow-rs kernels ([#5104](https://github.com/apache/datafusion-comet/issues/5104)) - Area labels: `EPIC`, `good first issue` - Rationale: Tracks refactoring duplicated logic onto arrow-rs kernels; every candidate was pre-screened for Spark-semantics equivalence, so behavior is intended to be unchanged. - Consolidate date/timestamp truncation and xxhash64 with upstream DataFusion implementations ([#5103](https://github.com/apache/datafusion-comet/issues/5103)) - Area labels: `area:expressions` - Rationale: Upstream consolidation of two hand-rolled implementations; refactor with no behavior change intended. - rlike: consider arrow regexp_is_match kernel and fix non-StringArray panic ([#5102](https://github.com/apache/datafusion-comet/issues/5102)) - Area labels: `area:expressions` - Rationale: Primarily a kernel-consolidation proposal with unresolved trade-offs (per-batch regex compilation); the `expect`-on-non-`StringArray` hardening it mentions is latent, since no current path feeds Utf8View/LargeUtf8 here. - list_extract: replace per-row MutableArrayData gather with take and zip ([#5100](https://github.com/apache/datafusion-comet/issues/5100)) - Area labels: `area:expressions` - Rationale: Replaces a hand-rolled per-row gather with `take` + `zip`; identical null propagation, expected performance win. - size: compute list sizes with the arrow length kernel ([#5099](https://github.com/apache/datafusion-comet/issues/5099)) - Area labels: `area:expressions` - Rationale: Vectorizes a per-row loop using the arrow `length` kernel with the same Spark legacy semantics. - sum_int: use arrow sum kernels and collapse integer type dispatch ([#5098](https://github.com/apache/datafusion-comet/issues/5098)) - Area labels: `area:aggregation` - Rationale: Collapses six duplicated dispatch blocks onto arrow's sum kernels; refactor plus SIMD win, with the accumulation-order edge case called out. - Use arrow dictionary casts instead of hand-rolled dictionary handling in cast paths ([#5096](https://github.com/apache/datafusion-comet/issues/5096)) - Area labels: `area:expressions` - Rationale: Replaces a degenerate hand-built dictionary and a manual unpack branch with arrow's dictionary cast; Spark-owned semantics stay in the value cast. - Delegate int/float/boolean to decimal cast arms to arrow safe cast ([#5095](https://github.com/apache/datafusion-comet/issues/5095)) - Area labels: `area:expressions` - Rationale: The arms were verified line-for-line identical to arrow-cast 58.4.0, so delegating is a refactor with bit-identical output. - decimal_rescale_check: replace fused rescale and precision check with arrow decimal cast ([#5094](https://github.com/apache/datafusion-comet/issues/5094)) - Area labels: `area:expressions` - Rationale: Arrow's decimal cast already does the rescale, HALF_UP rounding, and precision validation in one pass; replacing the hand-rolled version is a cleanup. - checked_arithmetic: delegate ANSI integer arithmetic to arrow checked kernels ([#5092](https://github.com/apache/datafusion-comet/issues/5092)) - Area labels: `area:expressions` - Rationale: Calls arrow's checked kernels via the `Datum` path instead of re-implementing them; the error remap and the non-replaceable float/Try branches stay. - Replace small hand-rolled element loops with arrow kernels and arity helpers ([#5091](https://github.com/apache/datafusion-comet/issues/5091)) - Area labels: `area:expressions` - Rationale: A batch of small equivalent-result loop replacements (decimal-to-boolean via `neq`, validity masks via `is_not_null`/`and`, `days_to_date`, pow arity). - Use arrow cast for hand-rolled temporal unit conversion loops ([#5090](https://github.com/apache/datafusion-comet/issues/5090)) - Area labels: `area:expressions`, `area:scan` - Rationale: The loops were verified bit-identical to arrow's cast against the arrow-cast 58.4.0 source; touches both the spark-expr conversions and the Parquet `cast_column` path. - Remove is_valid_decimal_precision duplicated from arrow-rs ([#5089](https://github.com/apache/datafusion-comet/issues/5089)) - Area labels: `area:expressions` - Rationale: Deletes a character-for-character copy of arrow's implementation whose own comment says to remove it once arrow-rs #6419 shipped; no semantic risk. - Triage the `spark.sql.legacy.*` configs dropped from the session-wide fallback in #4799 ([#5087](https://github.com/apache/datafusion-comet/issues/5087)) - Area labels: none - Rationale: An audit task to determine whether each remaining curated legacy config is respected or safe to ignore; no specific defect is asserted yet, so any bugs it uncovers should be filed separately. - Clean up dead ANSI-related plumbing (CometEvalMode helpers, inert allow_incompat on casts) ([#5077](https://github.com/apache/datafusion-comet/issues/5077)) - Area labels: `area:expressions` - Rationale: Removal or documentation of unreachable helpers and an inert serialized field; explicitly a cleanup, and it notes which error variants must be kept for other fixes. - ANSI mode test coverage: re-enable stale ignored tests and add missing cases ([#5076](https://github.com/apache/datafusion-comet/issues/5076)) - Area labels: `area:expressions`, `test` - Rationale: Test-coverage work — un-ignoring files whose blocking issue (#3375) is closed and adding missing ANSI cases; no product defect claimed. - Guard against silent fail_on_error loss in scalar function wiring ([#5074](https://github.com/apache/datafusion-comet/issues/5074)) - Area labels: `area:expressions` - Rationale: Hardening against a class of mistake that has happened once and is now fixed; the reporter verified no currently wired datafusion-spark function is ANSI-sensitive, so this is preventative. - Cast from float/double to decimal should return NULL for NaN/Infinity under ANSI mode ([#5069](https://github.com/apache/datafusion-comet/issues/5069)) - Area labels: `area:expressions` - Rationale: The pair is already marked `Incompatible` and requires `allowIncompatible=true`, so this removes one of the divergences blocking compatibility rather than fixing a promised behavior. - [EPIC] Complete interval type support: scan, operator gates, and remaining interval expressions ([#5061](https://github.com/apache/datafusion-comet/issues/5061)) - Area labels: `area:expressions`, `area:scan` - Rationale: Tracks completing interval support — mostly opening Scala-side type gates for functionality the native engine already handles; the individual hard failures it found are filed separately (#5058, #5059). - Support reading ANSI interval columns (YearMonthIntervalType / DayTimeIntervalType) in the native Parquet scan ([#5060](https://github.com/apache/datafusion-comet/issues/5060)) - Area labels: `area:scan` - Rationale: Unsupported scan types fall back cleanly today, as designed; adding them to the supported set is new functionality. ## Escalations to consider - Codegen dispatcher: whole-tree NullIntolerant short-circuit suppresses ANSI errors ([#5218](https://github.com/apache/datafusion-comet/issues/5218)) - Guide trigger: prioritization principle 1, "Correctness over crashes." This was filed as `priority:high` and has been raised to `priority:critical` because the short-circuit turns a Spark ANSI error into a silent NULL. A reviewer who reads the divergence as error-semantics-only (the reporter's framing: "values are unaffected") may want to move it back to `priority:high`. - Native Parquet schema-on-read diverges from Spark for ANSI interval targets ([#5188](https://github.com/apache/datafusion-comet/issues/5188)) - Guide trigger: correctness-over-crashes. Left at `priority:medium` because the wrong-results direction only occurs for a read Spark itself rejects with `PARQUET_COLUMN_DATA_TYPE_MISMATCH`, so no Spark-valid query gets bad data; a reviewer who counts silent reinterpretation of INT as microseconds as data corruption regardless of Spark's rejection should escalate to `priority:critical`. - fix: string-to-timestamp does not trim ISO control characters ([#5165](https://github.com/apache/datafusion-comet/issues/5165)) - Guide trigger: correctness-over-crashes. Comet returns NULL where Spark parses successfully, which is silent. Kept at `priority:medium` because the surface is control-character-padded input, but a reviewer who has seen this in real ingest data (logs, CSV) should escalate — note the same file's `date_parser` already trims correctly, so `CAST(... AS DATE)` and `CAST(... AS TIMESTAMP)` disagree. - Native make_date rejects Spark-valid years outside chrono range ([#5208](https://github.com/apache/datafusion-comet/issues/5208)) - Guide trigger: correctness-over-crashes. Silent NULL where Spark returns a date, so the decision tree reads critical, but the input range (years beyond ±262143) makes production exposure effectively nil; escalate if any real workload is found. - [EPIC] Memory pool and accounting audit sweep ([#5212](https://github.com/apache/datafusion-comet/issues/5212)) - Guide trigger: "A `priority:medium` bug ... affects a common workload → consider escalating." Already at `priority:high`; flagged here because finding 1 affects the **default** pool type (`fair_unified`) for every task, so a reviewer may want the individual sub-issues split out and prioritized independently rather than tracked only under the EPIC. ## Skipped — needs more info - Bug triage results: 2026-07-27 ([#5052](https://github.com/apache/datafusion-comet/issues/5052)) - Prior triage summary issue still carrying the auto-applied `requires-triage` label; not itself a bug or enhancement — a reviewer should sanity-check the referenced results and close it. - Bug triage results: 2026-07-20 ([#4980](https://github.com/apache/datafusion-comet/issues/4980)) - Prior triage summary issue still carrying the auto-applied `requires-triage` label; not itself a bug or enhancement — a reviewer should sanity-check the referenced results and close it. - Bug triage results: 2026-07-13 ([#4905](https://github.com/apache/datafusion-comet/issues/4905)) - Prior triage summary issue still carrying the auto-applied `requires-triage` label; not itself a bug or enhancement — a reviewer should sanity-check the referenced results and close it. - Bug triage results: 2026-07-06 ([#4838](https://github.com/apache/datafusion-comet/issues/4838)) - Prior triage summary issue still carrying the auto-applied `requires-triage` label; not itself a bug or enhancement — a reviewer should sanity-check the referenced results and close it. - Bug triage results: 2026-06-29 ([#4751](https://github.com/apache/datafusion-comet/issues/4751)) - Prior triage summary issue still carrying the auto-applied `requires-triage` label; not itself a bug or enhancement — a reviewer should sanity-check the referenced results and close it. -- This is an automated message from the Apache Git Service. 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